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"Testing and Evaluation"
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Challenges of remote assessment in higher education in the context of COVID-19: a case study of Middle East College
Due to the unprecedented COVID-19 incident, higher education institutions have faced different challenges in their teaching-learning activities. Particularly conducting assessments remotely during COVID-19 has posed extraordinary challenges for higher education institutions owing to lack of preparation superimposed with the inherent problems of remote assessment. In the current study, the challenges of remote assessment during COVID-19 incident in higher education institutions were investigated taking Middle East College as a case study. For the study, questionnaires were prepared and data from 50 faculties were collected and analyzed. The study focused on the challenges of remote assessment in general and academic dishonesty in particular. The main challenges identified in remote assessment were academic dishonesty, infrastructure, coverage of learning outcomes, and commitment of students to submit assessments. To minimize academic dishonesty, preparing different questions to each student was found to be the best approach. Online presentation was also found to be good option to control academic integrity violations. Combining various assessment methods, for instance report submission with online presentation, helps to minimize academic dishonesty since the examiner would have a chance to confirm whether the submitted work is the work of the student.
Journal Article
COVID-19 and schooling: evaluation, assessment and accountability in times of crises—reacting quickly to explore key issues for policy, practice and research with the school barometer
The crisis caused by the COVID-19 virus has far-reaching effects in the field of education, as schools were closed in March 2020 in many countries around the world. In this article, we present and discuss the School Barometer, a fast survey (in terms of reaction time, time to answer and dissemination time) that was conducted in Germany, Austria and Switzerland during the early weeks of the school lockdown to assess and evaluate the current school situation caused by COVID-19. Later, the School Barometer was extended to an international survey, and some countries conducted the survey in their own languages. In Germany, Austria and Switzerland, 7116 persons participated in the German language version: 2222 parents, 2152 students, 1949 school staff, 655 school leaders, 58 school authority and 80 members of the school support system. The aim was to gather, analyse and present data in an exploratory way to inform policy, practice and further research. In this article, we present some exemplary first results and possible implications for policy, practice and research. Furthermore, we reflect on the strengths and limitations of the School Barometer and fast surveys as well as the methodological options for data collection and analysis when using a short monitoring survey approach. Specifically, we discuss the methodological challenges associated with survey data of this kind, including challenges related to hypothesis testing, the testing of causal effects and approaches to ensure reliability and validity. By doing this, we reflect on issues of assessment, evaluation and accountability in times of crisis.
Journal Article
Multilingual frameworks : the construction and use of multilingual proficiency frameworks
\"Describes 20 years of work at Cambridge English to develop multilingual assessment frameworks. Multilingual Frameworks covers the development of the ALTE Framework and 'Can Do' project; work on the Common European Framework of Reference and the linking of the Cambridge English exam levels to it; Asset Languages - a major educational initiative for UK schools; and the European Survey on Language Competences. It proposes a model for the validity of assessment within a multilingual framework, and while illustrating the constraints which determined the approach taken to each project, makes clear recommendations on methodological good practice. It looks forward to the further extension of assessment frameworks to encompass a model for multilingual education\"-- Provided by publisher.
Book
Identification of Confirmatory Factor Analysis Models of Different Levels of Invariance for Ordered Categorical Outcomes
This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on this identified baseline model. This approach is not optimal because different identification conditions on this baseline model identify the scales of latent continuous responses in different ways. Once an invariance condition is imposed on a parameter, these identification conditions may become restrictions and define statistically non-equivalent models, leading to different conclusions. By analyzing the transformation that leaves the model-implied probabilities of response patterns unchanged, we give identification conditions for models with invariance of different types of parameters without referring to a specific parametrization of the baseline model. Tests based on this approach have the advantage that they do not depend on the specific identification condition chosen for the baseline model.
Journal Article
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package
lvnet
, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.
Journal Article
Student perceptions of assessment feedback: a critical scoping review and call for research
The potential of feedback to enhance students’ performance on a task, strategies, or learning has long been recognized in the literature. However, feedback needs to be utilized by a learner to realize its potential. Hence, examining student perceptions of feedback and their links to effective uptake of feedback has been the focus of much recent feedback research. This paper presents a critical scoping review of the feedback perceptions literature. The review discusses the methods employed by 164 studies published between 1987 and 2018 and synthesizes the main findings across this body of literature. Lacking theoretical frameworks, repetitiveness (not replicability) of studies, and methodological problems observed among the reviewed have resulted in somewhat disappointing conclusions. Based on the findings, we present a framework for future investigations into student perceptions of feedback and suggest several avenues for the future of the field.
Journal Article